Background2D

class gammapy.irf.Background2D(energy_lo, energy_hi, offset_lo, offset_hi, data, meta=None, interp_kwargs=None)

Bases: object

Background 2D.

Data format specification: BKG_2D

Parameters:

energy_lo, energy_hi : Quantity

Energy binning

offset_lo, offset_hi : Quantity

FOV coordinate offset-axis binning

data : Quantity

Background rate (usually: s^-1 MeV^-1 sr^-1)

Attributes Summary

default_interp_kwargs

Default Interpolation kwargs for NDDataArray.

Methods Summary

evaluate(self, fov_lon, fov_lat, energy_reco)	Evaluate at a given FOV position and energy.
evaluate_integrate(self, fov_lon, fov_lat, …)	Evaluate at given FOV position and energy, by integrating over the energy range.
from_hdulist(hdulist[, hdu])	Create from HDUList.
from_table(table)	Read from Table.
peek(self)
plot(self[, ax, add_cbar])	Plot energy offset dependence of the background model.
read(filename[, hdu])	Read from file.
to_3d(self)	Convert to Background3D.
to_fits(self[, name])	Convert to BinTableHDU.
to_table(self)	Convert to Table.

Attributes Documentation

default_interp_kwargs = {'bounds_error': False, 'fill_value': None}

Default Interpolation kwargs for NDDataArray. Extrapolate.

Methods Documentation

evaluate(self, fov_lon, fov_lat, energy_reco, method='linear', **kwargs)

Evaluate at a given FOV position and energy. The fov_lon, fov_lat, energy_reco has to have the same shape since this is a set of points on which you want to evaluate

To have the same API than background 3D for the background evaluation, the offset is fov_altaz_lon.

Parameters:

fov_lon, fov_lat : Angle

FOV coordinates expecting in AltAz frame, same shape than energy_reco

energy_reco : Quantity

Reconstructed energy, same dimension than fov_lat and fov_lat

method : str {‘linear’, ‘nearest’}, optional

Interpolation method

kwargs : dict

option for interpolation for RegularGridInterpolator

Returns:

array : Quantity

Interpolated values, axis order is the same as for the NDData array

evaluate_integrate(self, fov_lon, fov_lat, energy_reco, method='linear')

Evaluate at given FOV position and energy, by integrating over the energy range.

Parameters:

fov_lon, fov_lat : Angle

FOV coordinates expecting in AltAz frame.

energy_reco: `~astropy.units.Quantity`

Reconstructed energy edges.

method : {‘linear’, ‘nearest’}, optional

Interpolation method

Returns:

array : Quantity

Returns 2D array with axes offset

classmethod from_hdulist(hdulist, hdu='BACKGROUND')

Create from HDUList.

classmethod from_table(table)

Read from Table.

peek(self)

plot(self, ax=None, add_cbar=True, **kwargs)

Plot energy offset dependence of the background model.

classmethod read(filename, hdu='BACKGROUND')

Read from file.

to_3d(self)

Convert to Background3D.

Fill in a radially symmetric way.

to_fits(self, name='BACKGROUND')

Convert to BinTableHDU.

to_table(self)

Convert to Table.